{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eb7866fd-8081-41fd-a2db-b4cc68782831",
   "metadata": {},
   "source": [
    "# MIMIC-IV\n",
    "\n",
    "This notebook provides a few tables and figures which describe data elements in MIMIC-IV. In order to use the notebook you'll need to configure the Google Cloud SDK to run queries. An easy way to do this is to [auth the gcloud CLI](https://cloud.google.com/sdk/docs/authorizing). You will also need to use a Google account which has been granted access to query MIMIC-IV v2.0. See the [cloud access page](https://mimic.mit.edu/docs/gettingstarted/cloud/) on the MIMIC documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad3bfcf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from tableone import TableOne"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d9b1522-6b1f-4cd4-90cc-24f5ed833ffb",
   "metadata": {},
   "source": [
    "## Hospital stay demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62436abb-fe4c-4cec-8820-3e0eece716e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/requests/__init__.py:109: RequestsDependencyWarning: urllib3 (1.26.9) or chardet (5.0.0)/charset_normalizer (2.0.12) doesn't match a supported version!\n",
      "  warnings.warn(\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:18: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  PANDAS_GE_10 = str(pd.__version__) >= LooseVersion(\"1.0.0\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  other = LooseVersion(other)\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:19: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  PANDAS_GE_11 = str(pd.__version__) >= LooseVersion(\"1.1.0\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  other = LooseVersion(other)\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:20: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  PANDAS_GE_115 = str(pd.__version__) >= LooseVersion(\"1.1.5\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  other = LooseVersion(other)\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:21: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  PANDAS_GE_12 = str(pd.__version__) >= LooseVersion(\"1.2.0\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  other = LooseVersion(other)\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:29: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  SHAPELY_GE_17 = str(shapely.__version__) >= LooseVersion(\"1.7.0\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  other = LooseVersion(other)\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:30: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  SHAPELY_GE_18 = str(shapely.__version__) >= LooseVersion(\"1.8\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  other = LooseVersion(other)\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:31: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  SHAPELY_GE_20 = str(shapely.__version__) >= LooseVersion(\"2.0\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  other = LooseVersion(other)\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:227: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  PYPROJ_LT_3 = LooseVersion(pyproj.__version__) < LooseVersion(\"3\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:227: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  PYPROJ_LT_3 = LooseVersion(pyproj.__version__) < LooseVersion(\"3\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:228: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  PYPROJ_GE_31 = LooseVersion(pyproj.__version__) >= LooseVersion(\"3.1\")\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/geopandas/_compat.py:228: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  PYPROJ_GE_31 = LooseVersion(pyproj.__version__) >= LooseVersion(\"3.1\")\n"
     ]
    }
   ],
   "source": [
    "hosp = pd.read_gbq(\"\"\"\n",
    "SELECT\n",
    "      pat.subject_id\n",
    "    , adm.hadm_id\n",
    "    , DENSE_RANK() OVER hadm_window AS hosp_stay_num\n",
    "    , CASE\n",
    "        WHEN FIRST_VALUE(adm.hadm_id) OVER hadm_window = adm.hadm_id THEN 1\n",
    "        ELSE 0\n",
    "      END AS pat_count\n",
    "    , pat.anchor_age + (EXTRACT(YEAR FROM adm.admittime) - pat.anchor_year) AS age\n",
    "    , pat.gender\n",
    "    , adm.insurance\n",
    "    , DATETIME_DIFF(adm.dischtime, adm.admittime, HOUR) / 24 AS hosp_los\n",
    "    , pat.dod\n",
    "    , DATE_DIFF(pat.dod, CAST(adm.dischtime AS DATE), DAY) AS days_to_death\n",
    "    -- mortality flags\n",
    "    , CASE WHEN DATE_DIFF(pat.dod, CAST(adm.dischtime AS DATE), DAY) = 0 THEN 1 ELSE 0 END AS hospital_mortality\n",
    "FROM `physionet-data.mimiciv_hosp.patients` pat\n",
    "INNER JOIN `physionet-data.mimiciv_hosp.admissions` adm\n",
    "    ON pat.subject_id = adm.subject_id\n",
    "WINDOW hadm_window AS (PARTITION BY pat.subject_id ORDER BY adm.admittime)\n",
    "\"\"\", \"lcp-internal\")\n",
    "\n",
    "# add 1 year mortality\n",
    "hosp['one_year_mortality'] = hosp['days_to_death'].notnull().astype(int)\n",
    "\n",
    "# create a dataframe with the days to death for only the last ICU stay\n",
    "last_dod = hosp.groupby('subject_id')[['hosp_stay_num']].max().reset_index()\n",
    "last_dod = last_dod.merge(hosp[['subject_id', 'hosp_stay_num', 'days_to_death']], on=['subject_id', 'hosp_stay_num'], how='inner')\n",
    "last_dod.rename(columns={'days_to_death': 'days_to_death_last_stay_id'}, inplace=True)\n",
    "\n",
    "hosp = hosp.merge(last_dod, how='left', on=['subject_id', 'hosp_stay_num'])\n",
    "del last_dod\n",
    "hosp.sort_values(['subject_id', 'hosp_stay_num'], inplace=True)\n",
    "\n",
    "# fix some data type issues\n",
    "int_cols = hosp.dtypes.values==\"Int64\"\n",
    "hosp.loc[:, int_cols] = hosp.loc[:, int_cols].astype(float)\n",
    "hosp.loc[:, int_cols] = hosp.loc[:, int_cols].astype(int, errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26698a6f-ecae-4a7c-9a9e-5048ae035c35",
   "metadata": {},
   "source": [
    "## ICU demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "59064584",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_gbq(\"\"\"\n",
    "SELECT\n",
    "      pat.subject_id\n",
    "    , adm.hadm_id\n",
    "    , icu.stay_id\n",
    "    , ROW_NUMBER() OVER (PARTITION BY pat.subject_id ORDER BY icu.intime) AS icu_stay_num\n",
    "    , DENSE_RANK() OVER (PARTITION BY pat.subject_id ORDER BY adm.admittime) AS hosp_stay_num\n",
    "    , CASE\n",
    "        WHEN FIRST_VALUE(icu.stay_id) OVER icustay_window = icu.stay_id THEN 1\n",
    "        ELSE 0\n",
    "      END AS pat_count\n",
    "    , pat.anchor_age + (EXTRACT(YEAR FROM icu.intime) - pat.anchor_year) AS age\n",
    "    , pat.gender\n",
    "    , adm.insurance\n",
    "    , icu.first_careunit\n",
    "    , icu.los AS icu_los\n",
    "    , DATETIME_DIFF(adm.dischtime, adm.admittime, HOUR) / 24 AS hosp_los\n",
    "    , pat.dod\n",
    "    , DATE_DIFF(pat.dod, CAST(adm.dischtime AS DATE), DAY) AS days_to_death\n",
    "    -- mortality flags\n",
    "    , CASE WHEN DATE_DIFF(pat.dod, CAST(adm.dischtime AS DATE), DAY) = 0 THEN 1 ELSE 0 END AS hospital_mortality\n",
    "    , CASE WHEN DATE_DIFF(pat.dod, CAST(icu.outtime AS DATE), DAY) = 0 THEN 1 ELSE 0 END AS icu_mortality\n",
    "FROM `physionet-data.mimiciv_hosp.patients` pat\n",
    "INNER JOIN `physionet-data.mimiciv_hosp.admissions` adm\n",
    "    ON pat.subject_id = adm.subject_id\n",
    "INNER JOIN `physionet-data.mimiciv_icu.icustays` icu\n",
    "    ON adm.hadm_id = icu.hadm_id\n",
    "WINDOW hadm_window AS (PARTITION BY pat.subject_id ORDER BY adm.admittime)\n",
    "     , icustay_window AS (PARTITION BY pat.subject_id ORDER BY icu.intime)\n",
    "\"\"\", \"lcp-internal\")\n",
    "\n",
    "# add 1 year mortality\n",
    "data['one_year_mortality'] = data['days_to_death'].notnull().astype(int)\n",
    "\n",
    "# create a dataframe with the days to death for only the last ICU stay\n",
    "last_dod = data.groupby('subject_id')[['icu_stay_num']].max().reset_index()\n",
    "last_dod = last_dod.merge(data[['subject_id', 'icu_stay_num', 'days_to_death']], on=['subject_id', 'icu_stay_num'], how='inner')\n",
    "last_dod.rename(columns={'days_to_death': 'days_to_death_last_stay_id'}, inplace=True)\n",
    "\n",
    "data = data.merge(last_dod, how='left', on=['subject_id', 'icu_stay_num'])\n",
    "del last_dod\n",
    "data.sort_values(['subject_id', 'icu_stay_num'], inplace=True)\n",
    "\n",
    "# add a grouping variable for table one so we can have hospital mortality as a group and a row\n",
    "data['hosp_mort'] = data['hospital_mortality']\n",
    "\n",
    "# fix some data type issues\n",
    "int_cols = data.dtypes.values==\"Int64\"\n",
    "data.loc[:, int_cols] = data.loc[:, int_cols].astype(float)\n",
    "data.loc[:, int_cols] = data.loc[:, int_cols].astype(int, errors=\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbd8f3f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ICU demographics\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/tableone/tableone.py:991: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  df['percent'] = df['freq'].div(df.freq.sum(level=0),\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/tableone/tableone.py:1420: UserWarning: Order variable not found: hadm_count\n",
      "  warnings.warn(\"Order variable not found: {}\".format(k))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>76943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distinct patients, n (%)</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>53569 (69.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>64.7 (16.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administrative Gender, n (%)</th>\n",
       "      <th>F</th>\n",
       "      <td>0</td>\n",
       "      <td>34091 (44.3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Insurance, n (%)</th>\n",
       "      <th>Medicaid</th>\n",
       "      <td>0</td>\n",
       "      <td>5825 (7.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medicare</th>\n",
       "      <td></td>\n",
       "      <td>34740 (45.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td></td>\n",
       "      <td>36378 (47.3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hospital length of stay, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>11.0 (13.3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>In-hospital mortality, n (%)</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>8987 (11.7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One year mortality, n (%)</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>29747 (38.7)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />"
      ],
      "text/plain": [
       "                                            Missing       Overall\n",
       "n                                                           76943\n",
       "Distinct patients, n (%)           1              0  53569 (69.6)\n",
       "Age, mean (SD)                                    0   64.7 (16.9)\n",
       "Administrative Gender, n (%)       F              0  34091 (44.3)\n",
       "Insurance, n (%)                   Medicaid       0    5825 (7.6)\n",
       "                                   Medicare          34740 (45.2)\n",
       "                                   Other             36378 (47.3)\n",
       "Hospital length of stay, mean (SD)                0   11.0 (13.3)\n",
       "In-hospital mortality, n (%)       1              0   8987 (11.7)\n",
       "One year mortality, n (%)          1              0  29747 (38.7)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hospital demographics\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/tableone/tableone.py:991: FutureWarning: Using the level keyword in DataFrame and Series aggregations is deprecated and will be removed in a future version. Use groupby instead. df.sum(level=1) should use df.groupby(level=1).sum().\n",
      "  df['percent'] = df['freq'].div(df.freq.sum(level=0),\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/tableone/tableone.py:1420: UserWarning: Order variable not found: hadm_count\n",
      "  warnings.warn(\"Order variable not found: {}\".format(k))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>454324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distinct patients, n (%)</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>190279 (41.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>58.7 (19.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Administrative Gender, n (%)</th>\n",
       "      <th>F</th>\n",
       "      <td>0</td>\n",
       "      <td>237184 (52.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Insurance, n (%)</th>\n",
       "      <th>Medicaid</th>\n",
       "      <td>0</td>\n",
       "      <td>43748 (9.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medicare</th>\n",
       "      <td></td>\n",
       "      <td>168963 (37.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td></td>\n",
       "      <td>241613 (53.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hospital length of stay, mean (SD)</th>\n",
       "      <th></th>\n",
       "      <td>0</td>\n",
       "      <td>4.5 (6.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>In-hospital mortality, n (%)</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>9381 (2.1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>One year mortality, n (%)</th>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>112092 (24.7)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />"
      ],
      "text/plain": [
       "                                            Missing        Overall\n",
       "n                                                           454324\n",
       "Distinct patients, n (%)           1              0  190279 (41.9)\n",
       "Age, mean (SD)                                    0    58.7 (19.2)\n",
       "Administrative Gender, n (%)       F              0  237184 (52.2)\n",
       "Insurance, n (%)                   Medicaid       0    43748 (9.6)\n",
       "                                   Medicare          168963 (37.2)\n",
       "                                   Other             241613 (53.2)\n",
       "Hospital length of stay, mean (SD)                0      4.5 (6.6)\n",
       "In-hospital mortality, n (%)       1              0     9381 (2.1)\n",
       "One year mortality, n (%)          1              0  112092 (24.7)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = [\n",
    "    \"pat_count\",\n",
    "    # , \"hadm_count\",\n",
    "    \"age\", \"gender\", \"insurance\",\n",
    "    # \"first_careunit\",\n",
    "    # \"icu_los\",\n",
    "    \"hosp_los\",\n",
    "    # 'icu_mortality',\n",
    "    'hospital_mortality',\n",
    "    \"one_year_mortality\",\n",
    "    # \"days_to_death_last_stay_id\"\n",
    "]\n",
    "\n",
    "categorical = [\n",
    "    \"pat_count\",\n",
    "    # \"hadm_count\",\n",
    "    \"gender\", \"insurance\",\n",
    "    # \"first_careunit\",\n",
    "    # mortality flags\n",
    "    # 'icu_mortality',\n",
    "    'hospital_mortality',\n",
    "    'one_year_mortality',\n",
    "]\n",
    "\n",
    "order = {\n",
    "    \"pat_count\": [1, 0],\n",
    "    \"hadm_count\": [1, 0],\n",
    "    \"gender\": [\"F\", \"M\"],\n",
    "    # \"icu_mortality\": [1, 0],\n",
    "    \"hospital_mortality\": [1, 0],\n",
    "    \"one_year_mortality\": [1, 0],\n",
    "}\n",
    "\n",
    "limit = {\n",
    "    \"pat_count\": 1, \"hadm_count\": 1,\n",
    "    \"gender\": 1,\n",
    "    # \"icu_mortality\": 1,\n",
    "    \"hospital_mortality\": 1,\n",
    "    \"one_year_mortality\": 1,\n",
    "}\n",
    "\n",
    "rename = {\n",
    "    \"pat_count\": \"Distinct patients\", \"hadm_count\": \"Distinct hospitalizations\",\n",
    "    \"age\": \"Age\", \"gender\": \"Administrative Gender\", \"insurance\": \"Insurance\",\n",
    "    \"first_careunit\": \"First ICU stay, unit type\",\n",
    "    \"icu_los\": \"ICU length of stay\", \"hosp_los\": \"Hospital length of stay\",\n",
    "    \"icu_mortality\": \"In-ICU mortality\",\n",
    "    \"hospital_mortality\": \"In-hospital mortality\",\n",
    "    \"one_year_mortality\": \"One year mortality\",\n",
    "    # \"days_to_death_last_stay_id\": \"Time to death (days)\",\n",
    "}\n",
    "\n",
    "print('ICU demographics')\n",
    "icu_table = TableOne(data, columns=columns, categorical=categorical, order=order, limit=limit, rename=rename)\n",
    "display(icu_table)\n",
    "print('Hospital demographics')\n",
    "hosp_table = TableOne(hosp, columns=columns, categorical=categorical, order=order, limit=limit, rename=rename)\n",
    "display(hosp_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49e01057-751e-4df9-b3bf-dda65383719d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llll}\n",
      "\\toprule\n",
      "                          &   & Hospital stays &     ICU stays \\\\\n",
      "\\midrule\n",
      "n & {} &         454324 &         76943 \\\\\n",
      "Distinct patients, n (\\%) & 1 &  190279 (41.9) &  53569 (69.6) \\\\\n",
      "Age, mean (SD) &   &    58.7 (19.2) &   64.7 (16.9) \\\\\n",
      "Administrative Gender, n (\\%) & F &  237184 (52.2) &  34091 (44.3) \\\\\n",
      "Insurance, n (\\%) & Medicaid &    43748 (9.6) &    5825 (7.6) \\\\\n",
      "                          & Medicare &  168963 (37.2) &  34740 (45.2) \\\\\n",
      "                          & Other &  241613 (53.2) &  36378 (47.3) \\\\\n",
      "Hospital length of stay, mean (SD) &   &      4.5 (6.6) &   11.0 (13.3) \\\\\n",
      "In-hospital mortality, n (\\%) & 1 &     9381 (2.1) &   8987 (11.7) \\\\\n",
      "One year mortality, n (\\%) & 1 &  112092 (24.7) &  29747 (38.7) \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/fn/qznlq4rd1sn88j6llg9sqk1m0000gn/T/ipykernel_5638/4092134405.py:12: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(tableone_for_paper.to_latex())\n"
     ]
    }
   ],
   "source": [
    "# generate latex table used in sci data paper\n",
    "tableone_for_paper = hosp_table.tableone.drop(columns='Missing').copy()\n",
    "tableone_for_paper.rename(columns={'Overall': 'Hospital stays'}, inplace=True)\n",
    "\n",
    "tableone_for_paper = tableone_for_paper.merge(\n",
    "    icu_table.tableone.drop(columns='Missing'),\n",
    "    left_index=True, right_index=True, how='inner'\n",
    ")\n",
    "tableone_for_paper.rename(columns={'Overall': 'ICU stays'}, inplace=True)\n",
    "\n",
    "# TODO: merge 2nd col into 1st, which we did manually\n",
    "print(tableone_for_paper.to_latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b5c36ea-7d20-410f-b979-4ab7c922dadc",
   "metadata": {},
   "source": [
    "## Summary figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e53a3b31-035d-41f2-a907-9ff7d16e79b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# find a good example patient\n",
    "query = \"\"\"\n",
    "SELECT t.hadm_id\n",
    "from `physionet-data.mimiciv_hosp.admissions` t\n",
    "where \n",
    "-- has more than one icu stay for the hospitalization\n",
    "t.hadm_id IN (select hadm_id from `physionet-data.mimiciv_icu.icustays` group by 1 having count(*) > 1)\n",
    "AND \n",
    "-- has heparin admin\n",
    "t.hadm_id IN (SELECT DISTINCT hadm_id FROM `physionet-data.mimiciv_hosp.emar` WHERE LOWER(medication) LIKE '%drug%')\n",
    "AND\n",
    "-- did not die\n",
    "deathtime IS NULL\n",
    "-- maybe in trauma?\n",
    "\"\"\"\n",
    "df = pd.read_gbq(query, 'lcp-internal')\n",
    "# manually looked through df for an hadm_id which was visually appealing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1a46d2f-2c7a-4170-b11a-113145e98564",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hadm_id we extract / plot data for\n",
    "\n",
    "hadm_id = 28503629\n",
    "# above hadm_id - cardiac arrest in community\n",
    "# CCU on 2164-11-09T17:40:00\n",
    "# then got CABG and admitted to CVICU on 11-19\n",
    "# then discharged home"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf5bc310-3e4d-490b-89aa-f1688f121f0a",
   "metadata": {},
   "source": [
    "## Extract data\n",
    "\n",
    "Run SQL against BigQuery to grab dataframes with `hadm_id` specific data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "feb4e7f3-ce2e-4ebd-b360-d34d81598405",
   "metadata": {},
   "outputs": [],
   "source": [
    "# === laboratory derived datasets\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(bg.charttime, a.admittime, MINUTE) AS offset\n",
    "    , bg.po2 AS pao2\n",
    "    , bg.pco2 AS paco2\n",
    "    , bg.ph\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_derived.bg` bg\n",
    "  ON a.hadm_id = bg.hadm_id\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "AND bg.specimen = 'ART.'\n",
    "\"\"\"\n",
    "bg = pd.read_gbq(query, \"lcp-internal\")\n",
    "bg.sort_values('offset', inplace=True)\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(c.charttime, a.admittime, MINUTE) AS offset\n",
    "    , albumin\n",
    "    , globulin\n",
    "    , total_protein\n",
    "    , aniongap\n",
    "    , bicarbonate\n",
    "    , bun\n",
    "    , calcium\n",
    "    , chloride\n",
    "    , creatinine\n",
    "    , glucose\n",
    "    , sodium\n",
    "    , potassium\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_derived.chemistry` c\n",
    "  ON a.hadm_id = c.hadm_id\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "chem = pd.read_gbq(query, \"lcp-internal\")\n",
    "chem.sort_values('offset', inplace=True)\n",
    "\n",
    "query = f\"\"\"\n",
    "WITH trop AS\n",
    "(\n",
    "    SELECT specimen_id, MAX(valuenum) AS troponin_t\n",
    "    FROM `physionet-data.mimiciv_hosp.labevents`\n",
    "    WHERE itemid = 51003\n",
    "    GROUP BY specimen_id\n",
    ")\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(c.charttime, a.admittime, MINUTE) AS offset\n",
    "    , trop.troponin_t\n",
    "    , c.ntprobnp\n",
    "    , c.ck_mb\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "LEFT JOIN `physionet-data.mimiciv_derived.cardiac_marker` c\n",
    "  ON a.hadm_id = c.hadm_id\n",
    "LEFT JOIN trop\n",
    "  ON c.specimen_id = trop.specimen_id\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "cardiac = pd.read_gbq(query, \"lcp-internal\")\n",
    "cardiac.sort_values('offset', inplace=True)\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(cbc.charttime, a.admittime, MINUTE) AS offset\n",
    "    , cbc.wbc\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "LEFT JOIN `physionet-data.mimiciv_derived.complete_blood_count` cbc\n",
    "ON a.hadm_id = cbc.hadm_id\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "cbc = pd.read_gbq(query, \"lcp-internal\")\n",
    "cbc.sort_values('offset', inplace=True)\n",
    "\n",
    "\n",
    "# === icu information system derived dataframes\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(v.charttime, a.admittime, MINUTE) AS offset\n",
    "    , v.heart_rate, v.sbp, v.dbp\n",
    "    , v.sbp_ni, v.dbp_ni\n",
    "    , v.resp_rate\n",
    "    , v.temperature\n",
    "    , v.spo2\n",
    "    , v.glucose\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_icu.icustays` i\n",
    "    ON a.hadm_id = i.hadm_id\n",
    "LEFT JOIN `physionet-data.mimiciv_derived.vitalsign` v\n",
    "    ON i.stay_id = v.stay_id\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "vitals = pd.read_gbq(query, \"lcp-internal\")\n",
    "vitals.sort_values('offset', inplace=True)\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(v.charttime, a.admittime, MINUTE) AS offset\n",
    "    , v.heart_rhythm\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_icu.icustays` i\n",
    "    ON a.hadm_id = i.hadm_id\n",
    "LEFT JOIN `physionet-data.mimiciv_derived.rhythm` v\n",
    "    ON i.subject_id = v.subject_id\n",
    "    AND v.charttime BETWEEN i.intime AND i.outtime\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "rhythm = pd.read_gbq(query, \"lcp-internal\")\n",
    "rhythm.sort_values('offset', inplace=True)\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(gcs.charttime, a.admittime, MINUTE) AS offset\n",
    "    , gcs.gcs_motor\n",
    "    , gcs.gcs_verbal\n",
    "    , gcs.gcs_eyes\n",
    "    , gcs.gcs_unable\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_derived.gcs` gcs\n",
    "  ON a.subject_id = gcs.subject_id\n",
    "  AND gcs.charttime BETWEEN a.admittime AND a.dischtime\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "gcs = pd.read_gbq(query, \"lcp-internal\")\n",
    "gcs.sort_values('offset', inplace=True)\n",
    "\n",
    "# separately extract temperature from vital signs\n",
    "# the temperature vital sign as of the time of this notebook did not include arctic sun itemids\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    c.hadm_id\n",
    "    , DATETIME_DIFF(c.charttime, a.admittime, MINUTE) AS offset\n",
    "    , di.label\n",
    "    , c.value\n",
    "    , c.valuenum\n",
    "    , c.valueuom\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_icu.chartevents` c\n",
    "  ON a.hadm_id = c.hadm_id\n",
    "INNER JOIN `physionet-data.mimiciv_icu.d_items` di\n",
    "  ON c.itemid = di.itemid\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "AND c.itemid IN\n",
    "(\n",
    "227632, -- Arctic Sun/Alsius Temp #1 C\n",
    "227634, -- Arctic Sun/Alsius Temp #2 C\n",
    "223761 -- Temperature Fahrenheit\n",
    ")\n",
    "AND valuenum > 10 AND valuenum < 120\n",
    "\"\"\"\n",
    "temp = pd.read_gbq(query, \"lcp-internal\")\n",
    "# convert farenheit\n",
    "idx = temp['valueuom'].isin(['°F'])\n",
    "temp.loc[idx, 'valuenum'] = (temp.loc[idx, 'valuenum'] - 32)/1.8\n",
    "temp.sort_values('offset', inplace=True)\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(inp.starttime, a.admittime, MINUTE) AS start_offset\n",
    "    , DATETIME_DIFF(inp.endtime, a.admittime, MINUTE) AS end_offset\n",
    "    , di.label\n",
    "    , inp.rate\n",
    "    , inp.amount\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_icu.icustays` i\n",
    "    ON a.hadm_id = i.hadm_id\n",
    "LEFT JOIN `physionet-data.mimiciv_icu.inputevents` inp\n",
    "    ON i.stay_id = inp.stay_id\n",
    "LEFT JOIN `physionet-data.mimiciv_icu.d_items` di\n",
    "    ON inp.itemid = di.itemid\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "inp = pd.read_gbq(query, \"lcp-internal\")\n",
    "inp.sort_values('start_offset', inplace=True)\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(pe.starttime, a.admittime, MINUTE) AS start_offset\n",
    "    , DATETIME_DIFF(pe.endtime, a.admittime, MINUTE) AS end_offset\n",
    "    , di.label\n",
    "    , pe.value\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_icu.icustays` i\n",
    "    ON a.hadm_id = i.hadm_id\n",
    "LEFT JOIN `physionet-data.mimiciv_icu.procedureevents` pe\n",
    "    ON i.stay_id = pe.stay_id\n",
    "LEFT JOIN `physionet-data.mimiciv_icu.d_items` di\n",
    "    ON pe.itemid = di.itemid\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "pe = pd.read_gbq(query, \"lcp-internal\")\n",
    "pe.sort_values('start_offset', inplace=True)\n",
    "\n",
    "\n",
    "# === provider order entry (poe)\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(p.ordertime, a.admittime, MINUTE) AS offset\n",
    "    , p.poe_id\n",
    "    , p.order_type, p.order_subtype\n",
    "    , p.transaction_type\n",
    "    , pd.field_name\n",
    "    , pd.field_value\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_hosp.poe` p\n",
    "    ON a.hadm_id = p.hadm_id\n",
    "LEFT JOIN  `physionet-data.mimiciv_hosp.poe_detail` pd\n",
    "    ON p.poe_id = pd.poe_id\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "poe = pd.read_gbq(query, \"lcp-internal\")\n",
    "poe.sort_values('offset', inplace=True)\n",
    "# poe.groupby(['order_type', 'order_subtype'])[['hadm_id', 'field_name']].count()\n",
    "\n",
    "# === billed ICD procedures\n",
    "query = f\"\"\"\n",
    "select pi.hadm_id\n",
    ", DATE_DIFF(pi.chartdate, a.admittime, DAY) AS offset_days\n",
    ", pi.icd_code\n",
    ", pi.icd_version\n",
    ", di.long_title\n",
    "FROM `lcp-internal.mimiciv_hosp.procedures_icd` pi\n",
    "INNER JOIN `lcp-internal.mimiciv_hosp.d_icd_procedures` di\n",
    "  ON pi.icd_version = di.icd_version\n",
    "  AND pi.icd_code = di.icd_code\n",
    "INNER JOIN `physionet-data.mimiciv_hosp.admissions` a\n",
    "  ON pi.hadm_id = a.hadm_id\n",
    "where pi.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "proc = pd.read_gbq(query, \"lcp-internal\")\n",
    "proc['offset'] = proc['offset_days']*24*60\n",
    "proc.sort_values('offset', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9feab06f-7546-49ef-abab-b8a575fdbd55",
   "metadata": {},
   "source": [
    "Other relevant data which we did not end up using in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b77fdfb-d1b3-41d4-8e7a-c94ea66cc278",
   "metadata": {},
   "outputs": [],
   "source": [
    "# arctic sun - used for cooling\n",
    "query = f\"\"\"\n",
    "WITH pivot AS\n",
    "(\n",
    "    SELECT c.hadm_id, c.charttime\n",
    "    , ANY_VALUE(CASE WHEN c.itemid = 224106 THEN value ELSE NULL END) AS cooling_device\n",
    "    , ANY_VALUE(CASE WHEN c.itemid = 225052 THEN value ELSE NULL END) AS cooling_device_status\n",
    "    FROM `physionet-data.mimiciv_icu.chartevents` c\n",
    "    INNER JOIN `physionet-data.mimiciv_icu.d_items` di\n",
    "      ON c.itemid = di.itemid\n",
    "    WHERE c.hadm_id = {hadm_id}\n",
    "    AND c.itemid IN\n",
    "    (\n",
    "    224106, -- Cooling Device\n",
    "    225052 -- Cooling Device Status\n",
    "    )\n",
    "    GROUP BY 1, 2\n",
    ")\n",
    "SELECT\n",
    "    a.hadm_id\n",
    "    , DATETIME_DIFF(charttime, a.admittime, MINUTE) AS offset\n",
    "    , cooling_device, cooling_device_status\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN pivot\n",
    "    ON a.hadm_id = pivot.hadm_id\n",
    "\"\"\"\n",
    "cooling = pd.read_gbq(query, \"lcp-internal\")\n",
    "cooling = cooling.sort_values('offset')\n",
    "\n",
    "# filter to arctic sun\n",
    "cooling = cooling.loc[(cooling['cooling_device'] == 'Arctic Sun') & (cooling['cooling_device_status'] == 'On')]\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT\n",
    "    c.hadm_id\n",
    "    , DATETIME_DIFF(c.charttime, a.admittime, MINUTE) AS offset\n",
    "    , di.label\n",
    "    , c.value\n",
    "    , c.valuenum\n",
    "FROM `physionet-data.mimiciv_hosp.admissions` a\n",
    "INNER JOIN `physionet-data.mimiciv_icu.chartevents` c\n",
    "  ON a.hadm_id = c.hadm_id\n",
    "INNER JOIN `physionet-data.mimiciv_icu.d_items` di\n",
    "  ON c.itemid = di.itemid\n",
    "WHERE a.hadm_id = {hadm_id}\n",
    "AND c.itemid IN\n",
    "(\n",
    "  228332, -- Delirium assessment\n",
    "  228688, -- Delirium\n",
    "  228301, -- CAM-ICU Inattention\n",
    "  228302, -- CAM-ICU RASS LOC\n",
    "  228303, -- CAM-ICU Disorganized thinking\n",
    "  228334, -- CAM-ICU Altered LOC\n",
    "  228335, -- CAM-ICU Disorganized thinking\n",
    "  228336, -- CAM-ICU Inattention\n",
    "  228337, -- CAM-ICU MS Change\n",
    "  229324, -- CAM-ICU Disorganized thinking\n",
    "  229325, -- CAM-ICU Inattention\n",
    "  229326, -- CAM-ICU MS Change\n",
    "  228300  -- CAM-ICU MS change\n",
    ")\n",
    "\"\"\"\n",
    "delirium = pd.read_gbq(query, \"lcp-internal\")\n",
    "delirium.sort_values('offset', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb3cb0ba-70b8-4121-8ad7-05c04be9df58",
   "metadata": {},
   "source": [
    "### figure 1 - patient data\n",
    "\n",
    "To simplify plotting, we create a few plotting methods for the different visualizations of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "589e9f92-a6cb-40a0-bb0d-877ec8ee3eb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/seaborn/rcmod.py:400: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  if LooseVersion(mpl.__version__) >= \"3.0\":\n",
      "/Users/alistairewj/miniconda3/envs/python39/lib/python3.9/site-packages/setuptools/_distutils/version.py:351: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  other = LooseVersion(other)\n"
     ]
    },
    {
     "data": {
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       "['#0173b2',\n",
       " '#de8f05',\n",
       " '#029e73',\n",
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def plot_numeric_data(ax, df_input, plot_info, null_time=None):\n",
    "    for i, (column, plot_args) in enumerate(plot_info.items()):\n",
    "        df = df_input[['offset', column]].copy().dropna()\n",
    "        \n",
    "        # insert a null when the patient leaves the ICU to break the line in the plot\n",
    "        if null_time is not None:\n",
    "            dff = pd.DataFrame([[null_time, None]], columns=['offset', column])\n",
    "            df = pd.concat([df, dff], ignore_index=True)\n",
    "        df.sort_values('offset', inplace=True)\n",
    "        ax.plot(df['offset']/TIME_COEF, df[column], **plot_args)\n",
    "\n",
    "def plot_range_data(ax, df_input, plot_info, null_time=None):\n",
    "    for i, (column, plot_args) in enumerate(plot_info.items()):\n",
    "        idx = df_input['label'] == column\n",
    "        df = df_input.loc[idx, ['start_offset', 'end_offset']].copy().dropna()\n",
    "        # specify the y-axis value to avoid visual collisions\n",
    "        df['value'] = plot_args.pop('y_value')\n",
    "        df.sort_values('start_offset', inplace=True)\n",
    "\n",
    "        # annotate the plot with text from the field_value\n",
    "        for _, row in df.iterrows():\n",
    "            ax.plot(\n",
    "                [row['start_offset']/TIME_COEF, row['end_offset']/TIME_COEF],\n",
    "                2*[row['value']],\n",
    "                **plot_args\n",
    "            )\n",
    "            # below would add text next to each event\n",
    "            # ax.text(row['start_offset']/TIME_COEF, row['value'], column, fontsize=18, va='center')\n",
    "\n",
    "def plot_numeric_as_text(ax, df_input, plot_info, null_time=None, first_only=False):\n",
    "    for i, (column, plot_args) in enumerate(plot_info.items()):\n",
    "        df = df_input[['offset', column]].copy().dropna()\n",
    "        y_value = plot_args.pop('y_value')\n",
    "        \n",
    "        for _, row in df.sort_values('offset').iterrows():\n",
    "            text = row[column]\n",
    "            ax.plot(row['offset']/TIME_COEF, y_value, **plot_args)\n",
    "            ax.text(row['offset']/TIME_COEF, y_value, text, rotation=30, fontsize=18)\n",
    "\n",
    "def plot_rate_as_text(ax, df_input, plot_info, null_time=None, rate_col='rate'):\n",
    "    for i, (label, plot_args) in enumerate(plot_info.items()):\n",
    "        idx = df_input['label'] == label\n",
    "        df = df_input.loc[idx, ['start_offset', 'end_offset', rate_col]].copy().dropna()\n",
    "        y_value = plot_args.pop('y_value')\n",
    "        \n",
    "        if df.shape[0] == 0:\n",
    "            continue\n",
    "\n",
    "        # plot the rate going up and down if the delivery goes up and down\n",
    "        # but squeeze the values plotted to span 1 unit on the graph\n",
    "        multiplier = 1\n",
    "        min_val, max_val = df[rate_col].min(), df[rate_col].max()\n",
    "        if max_val != min_val:\n",
    "            multiplier = 1/(max_val - min_val)\n",
    "        \n",
    "        \n",
    "        for _, row in df.sort_values('start_offset').iterrows():\n",
    "            start, end = row['start_offset']/TIME_COEF, row['end_offset']/TIME_COEF\n",
    "            value = (row[rate_col] - min_val) * multiplier + y_value\n",
    "            ax.plot([start, end], [value, value], **plot_args)\n",
    "        \n",
    "        start = df.iloc[0]['start_offset']/TIME_COEF\n",
    "        ax.text(start, y_value, label, rotation=30, fontsize=18)\n",
    "\n",
    "def plot_text_data(ax, df_input, plot_info, text_col='value', null_time=None, label_col=None):\n",
    "    for i, (label, plot_args) in enumerate(plot_info.items()):\n",
    "        if label_col is None:\n",
    "            # use all rows\n",
    "            idx = df_input['offset'].notnull()\n",
    "        else:\n",
    "            idx = df_input[label_col] == label\n",
    "\n",
    "        # specify the y-axis value to avoid visual collisions\n",
    "        y_value = plot_args.pop('y_value')\n",
    "\n",
    "        for _, row in df_input.loc[idx].sort_values('offset').iterrows():\n",
    "            # split text into multiple lines if it's too long - every 4 words\n",
    "            text = row[text_col].split(' ')\n",
    "            text = '\\n'.join([' '.join(text[i:i+4]) for i in range(0, len(text), 4)])\n",
    "            ax.plot(row['offset']/TIME_COEF, y_value, **plot_args)\n",
    "            ax.text(row['offset']/TIME_COEF + 0.1, y_value, text, va='center', fontsize=18)\n",
    "\n",
    "# define colors for the next two figures\n",
    "sns.set_theme(context='paper', font_scale=2.5, style=\"whitegrid\", palette=\"pastel\")\n",
    "pal = sns.color_palette('colorblind', 10)\n",
    "colors = pal.as_hex()\n",
    "colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "98c5b0b7-3fda-4811-8291-6e9227bd99a4",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1584x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert times to a unit consistently\n",
    "# base time is minutes, so 24*60 -> equiv to converting to days\n",
    "TIME_COEF = 24*60.0\n",
    "TIME_UNIT = 'days'\n",
    "\n",
    "# to prevent a line being drawn between distinct ICU stays, we sometimes add a NaN row to data before plotting\n",
    "null_time = 8*24*60\n",
    "\n",
    "fig = plt.figure(figsize=(22, 20))\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "gs = fig.add_gridspec(3, hspace=0)\n",
    "ax0, ax1, ax2 = gs.subplots(sharex=True, sharey=False)\n",
    "\n",
    "ax0.set_xlim([0, 13])\n",
    "\n",
    "# === vital signs\n",
    "plot_info_vitals = OrderedDict([\n",
    "    ['heart_rate', {'label': 'Heart Rate', 'color': colors[0], 'marker': 'o', 'lw': 2.5, 'markersize': 6}],\n",
    "    ['resp_rate', {'label': 'Respiratory Rate', 'color': colors[1], 'marker': 'o', 'lw': 2.5, 'markersize': 6}],\n",
    "])\n",
    "plot_numeric_data(ax0, vitals, plot_info_vitals, null_time=null_time)\n",
    "\n",
    "\n",
    "# plot shaded dbp/sbp data\n",
    "bp = vitals[['offset', 'dbp', 'sbp']].dropna().copy()\n",
    "idx = bp['offset'] < null_time\n",
    "plot_args = {'color': colors[3], 'alpha': 0.2}\n",
    "# first half, before ICU discharge\n",
    "ax0.fill_between(bp.loc[idx, 'offset']/TIME_COEF, bp.loc[idx, 'dbp'], bp.loc[idx, 'sbp'], label='Blood Pressure (mmHg)', **plot_args)\n",
    "ax0.fill_between(bp.loc[~idx, 'offset']/TIME_COEF, bp.loc[~idx, 'dbp'], bp.loc[~idx, 'sbp'], **plot_args)\n",
    "\n",
    "# plot temperature\n",
    "idx = temp['offset'] < null_time\n",
    "ax0.plot(temp.loc[idx, 'offset']/TIME_COEF, temp.loc[idx, 'valuenum'], label='Temperature (°C)', color=colors[2], marker='d', markersize=10, lw=2)\n",
    "ax0.plot(temp.loc[~idx, 'offset']/TIME_COEF, temp.loc[~idx, 'valuenum'], color=colors[2], marker='d', markersize=10, lw=2)\n",
    "\n",
    "# add the legend to the plot itself for better readability\n",
    "x_loc = 5.15\n",
    "ax0.text(x_loc, 110, 'Blood Pressure (mmHg)', color=colors[3])\n",
    "ax0.text(x_loc, 88, 'Heart Rate (beats/minute)', color=colors[0])\n",
    "ax0.text(x_loc, 36, 'Temperature (°C)', color=colors[2])\n",
    "ax0.text(x_loc, 14, 'Respiration rate (breaths/minute)', color=colors[1])\n",
    "ax0.grid(False, axis='x')\n",
    "\n",
    "plot_info_cardiac = OrderedDict([\n",
    "    ['troponin_t', {'label': 'Troponin-T', 'y_value': 0, 'color': colors[4], 'marker': 'o', 'markersize': 10}],\n",
    "    ['ck_mb', {'label': 'CK-MB', 'y_value': 1, 'color': colors[5], 'marker': 'd', 'markersize': 10}],\n",
    "])\n",
    "plot_numeric_as_text(ax1, cardiac.iloc[0::2], plot_info_cardiac, null_time=null_time)\n",
    "\n",
    "ax1.text(4, 0, 'Troponin-T (ng/mL)', va='center', color=colors[4])\n",
    "ax1.text(4, 1, 'CK-MB (ng/mL)', va='center', color=colors[5])\n",
    "\n",
    "\n",
    "# arctic sun\n",
    "#plot_info_cooling = OrderedDict([\n",
    "#    ['value', {'label': 'Arctic Sun (on)', 'color': colors[4], 'linewidth': 0, 'markersize': 6, 'marker': 'o'}],\n",
    "#])\n",
    "#cooling_plot = cooling.copy()\n",
    "#cooling_plot['value'] = 2\n",
    "#plot_numeric_data(ax1, cooling_plot, plot_info_cooling, null_time=null_time)\n",
    "\n",
    "\n",
    "plot_info_pe = OrderedDict([\n",
    "    # ['General Xray', {'y_value': 150, 'label': 'X-ray', 'color': colors[0], 'lw': 0, 'marker': 'o'}],\n",
    "    ['Invasive Ventilation', {'y_value': 3, 'label': 'Ventilation', 'color': colors[0], 'lw': 2, 'marker': 'o'}],\n",
    "    ['Foley Catheter', {'y_value': 4, 'label': 'Foley Catheter', 'color': colors[1], 'lw': 2, 'marker': 'o'}],\n",
    "    #['Tubes/Drains', {'y_value': 170, 'label': 'Tubes/Drains', 'color': colors[1], 'lw': 0, 'marker': 'o'}],\n",
    "])\n",
    "plot_range_data(ax1, pe, plot_info_pe, null_time=null_time)\n",
    "\n",
    "# labels for vent/foley\n",
    "ax1.text(3.2, 3, 'Invasive Ventilation', va='center', color=colors[0])\n",
    "ax1.text(5.1, 4, 'Foley Catheter', va='center', color=colors[1])\n",
    "\n",
    "# POE events\n",
    "plot_info_poe = OrderedDict([\n",
    "    ['ECG', {'label': 'TBD', 'y_value': 4.5, 'marker': 'o', 'color': colors[7]}],\n",
    "    ['Extubate', {'label': 'TBD', 'y_value': 5.5, 'marker': 'o', 'color': colors[7]}],\n",
    "])\n",
    "plot_text_data(ax1, poe, plot_info_poe, text_col='order_subtype', null_time=null_time, label_col='order_subtype')\n",
    "\n",
    "\n",
    "proc_subset = proc.iloc[[0, 2]].copy().rename(columns={'long_title': 'value'})\n",
    "plot_info_proc = OrderedDict([\n",
    "    ['proc', {'label': 'Billed Procedures', 'y_value': 7, 'marker': 'o', 'color': colors[7]}],\n",
    "])\n",
    "plot_text_data(ax1, proc_subset, plot_info_proc, null_time=null_time)\n",
    "\n",
    "ax1.set_ylim([-0.5, 8.5])\n",
    "\n",
    "\n",
    "ax1.set_yticks([2, 6], ['', ''])\n",
    "ax1.grid(False, axis='x')\n",
    "\n",
    "# === blood gases\n",
    "#plot_info_bg = OrderedDict([\n",
    "#    ['pao2', {'label': 'PaO2', 'color': colors[3], 'marker': 'o', 'lw': 2.5, 'markersize': 6}],\n",
    "#    ['paco2', {'label': 'PaCO2', 'color': colors[1], 'marker': 'o', 'lw': 2.5, 'markersize': 6}],\n",
    "#])\n",
    "# plot_numeric_data(ax2, bg, plot_info_bg, null_time=null_time)\n",
    "\n",
    "plot_info_chem = OrderedDict([\n",
    "    ['sodium', {'label': 'Sodium (mEq/L)', 'color': colors[0], 'marker': 's', 'lw': 2.5, 'markersize': 8}],\n",
    "    ['potassium', {'label': 'Potassium (mEq/L)', 'color': colors[4], 'marker': 's', 'lw': 2.5, 'markersize': 6}],\n",
    "    ['glucose', {'label': 'Glucose (mg/dL)', 'color': colors[2], 'marker': '^', 'lw': 2.5, 'markersize': 8}],\n",
    "    ['bun', {'label': 'Blood Urea Nitrogen (mg/dL)', 'color': colors[3], 'marker': 'd', 'lw': 2.5, 'markersize': 6}],\n",
    "    ['creatinine', {'label': 'Creatinine (mg/dL)', 'color': colors[1], 'marker': 'o', 'lw': 2.5, 'markersize': 8}],\n",
    "    ['bicarbonate', {'label': 'Bicarbonate (mEq/L)', 'color': colors[9], 'marker': 'o', 'lw': 2.5, 'markersize': 6}],\n",
    "    ['chloride', {'label': 'Chloride (mEq/L)', 'color': colors[5], 'marker': 'o', 'lw': 2.5, 'markersize': 6}],\n",
    "])\n",
    "plot_numeric_data(ax2, chem, plot_info_chem, null_time=None)\n",
    "\n",
    "ax2.set_ylim([-10, 160])\n",
    "# labels for chem7\n",
    "lab_locs = OrderedDict([\n",
    "    ['sodium', (6, 130)],\n",
    "    ['potassium', (6, 8.8)],\n",
    "    ['glucose', (6, 80)],\n",
    "    ['bun', (3, 55)],\n",
    "    ['creatinine', (6, -6)],\n",
    "    ['bicarbonate', (6, 55)],\n",
    "    ['chloride', (6, 110)],\n",
    "])\n",
    "for lab, (x_loc, y_loc) in lab_locs.items():\n",
    "    label = plot_info_chem[lab]['label']\n",
    "    color = plot_info_chem[lab]['color']\n",
    "    # for bun / bicarbonate, we add an annotation line\n",
    "    if lab in ('bun', 'bicarbonate'):\n",
    "        if lab == 'bun':\n",
    "            xy = (2.499 + 0.02, 31 + 1)\n",
    "        else:\n",
    "            xy = (5.52 + 0.02, 26 + 1)\n",
    "        \n",
    "        ax2.annotate(label, xy, (x_loc, y_loc), va='center', ha='center', color=color, arrowprops={'width': 3, 'color': color})\n",
    "    else:\n",
    "        ax2.text(x_loc, y_loc, label, va='center', ha='center', color=color)\n",
    "        \n",
    "\n",
    "ax2.grid(False, axis='x')\n",
    "\n",
    "# save!\n",
    "fig.savefig('mimiciv_example_patient.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5a1cbd-bb72-4060-9d80-c68dcbd9cb9b",
   "metadata": {},
   "source": [
    "## Medications\n",
    "\n",
    "We plot different medication sources for the same patient to help visualize their overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c52e7bef-eb61-452a-99f4-5796007c74f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pr: Index(['subject_id', 'hadm_id', 'pharmacy_id', 'poe_id', 'poe_seq',\n",
      "       'starttime', 'stoptime', 'drug_type', 'drug', 'formulary_drug_cd',\n",
      "       'gsn', 'ndc', 'prod_strength', 'form_rx', 'dose_val_rx', 'dose_unit_rx',\n",
      "       'form_val_disp', 'form_unit_disp', 'doses_per_24_hrs', 'route',\n",
      "       'offset', 'stop_offset'],\n",
      "      dtype='object')\n",
      "ph: Index(['subject_id', 'hadm_id', 'pharmacy_id', 'poe_id', 'starttime',\n",
      "       'stoptime', 'medication', 'proc_type', 'status', 'entertime',\n",
      "       'verifiedtime', 'route', 'frequency', 'disp_sched', 'infusion_type',\n",
      "       'sliding_scale', 'lockout_interval', 'basal_rate', 'one_hr_max',\n",
      "       'doses_per_24_hrs', 'duration', 'duration_interval', 'expiration_value',\n",
      "       'expiration_unit', 'expirationdate', 'dispensation', 'fill_quantity',\n",
      "       'offset', 'stop_offset'],\n",
      "      dtype='object')\n",
      "emar: Index(['subject_id', 'hadm_id', 'emar_id', 'emar_seq', 'poe_id', 'pharmacy_id',\n",
      "       'charttime', 'medication', 'event_txt', 'scheduletime', 'storetime',\n",
      "       'offset'],\n",
      "      dtype='object')\n",
      "emardet: Index(['subject_id', 'emar_id', 'emar_seq', 'parent_field_ordinal',\n",
      "       'administration_type', 'pharmacy_id', 'barcode_type',\n",
      "       'reason_for_no_barcode', 'complete_dose_not_given', 'dose_due',\n",
      "       'dose_due_unit', 'dose_given', 'dose_given_unit',\n",
      "       'will_remainder_of_dose_be_given', 'product_amount_given',\n",
      "       'product_unit', 'product_code', 'product_description',\n",
      "       'product_description_other', 'prior_infusion_rate', 'infusion_rate',\n",
      "       'infusion_rate_adjustment', 'infusion_rate_adjustment_amount',\n",
      "       'infusion_rate_unit', 'route', 'infusion_complete',\n",
      "       'completion_interval', 'new_iv_bag_hung',\n",
      "       'continued_infusion_in_other_location', 'restart_interval', 'side',\n",
      "       'site', 'non_formulary_visual_verification'],\n",
      "      dtype='object')\n",
      "inp: Index(['subject_id', 'hadm_id', 'stay_id', 'starttime', 'endtime', 'storetime',\n",
      "       'itemid', 'amount', 'amountuom', 'rate', 'rateuom', 'orderid',\n",
      "       'linkorderid', 'ordercategoryname', 'secondaryordercategoryname',\n",
      "       'ordercomponenttypedescription', 'ordercategorydescription',\n",
      "       'patientweight', 'totalamount', 'totalamountuom', 'isopenbag',\n",
      "       'continueinnextdept', 'statusdescription', 'originalamount',\n",
      "       'originalrate', 'offset', 'stop_offset'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "def add_offset_column(df, admittime, col='charttime', offset_col='offset', time_coef=24*60):\n",
    "    df[offset_col] = ((pd.to_datetime(df[col]) - admittime) / np.timedelta64(1, 'm')) / time_coef\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT *\n",
    "FROM `physionet-data.mimiciv_hosp.admissions`\n",
    "WHERE hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "adm = pd.read_gbq(query, \"lcp-internal\").sort_values('admittime')\n",
    "admittime = pd.to_datetime(adm.loc[0, 'admittime'])\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT *\n",
    "FROM `physionet-data.mimiciv_hosp.prescriptions`\n",
    "WHERE hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "pr = pd.read_gbq(query, \"lcp-internal\").sort_values('starttime')\n",
    "# subtract off admittime to create offset - operates on dataframe in-place\n",
    "add_offset_column(pr, admittime, 'starttime', 'offset')\n",
    "add_offset_column(pr, admittime, 'stoptime', 'stop_offset')\n",
    "print(f'pr: {pr.columns}')\n",
    "\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT *\n",
    "FROM `physionet-data.mimiciv_hosp.pharmacy`\n",
    "WHERE hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "ph = pd.read_gbq(query, \"lcp-internal\").sort_values('starttime')\n",
    "# subtract off admittime to create offset - operates on dataframe in-place\n",
    "add_offset_column(ph, admittime, 'starttime', 'offset')\n",
    "add_offset_column(ph, admittime, 'stoptime', 'stop_offset')\n",
    "print(f'ph: {ph.columns}')\n",
    "\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT *\n",
    "FROM `physionet-data.mimiciv_hosp.emar`\n",
    "WHERE hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "emar = pd.read_gbq(query, \"lcp-internal\").sort_values('charttime')\n",
    "add_offset_column(emar, admittime, 'charttime', 'offset')\n",
    "print(f'emar: {emar.columns}')\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT det.*\n",
    "FROM `physionet-data.mimiciv_hosp.emar_detail` det\n",
    "INNER JOIN `physionet-data.mimiciv_hosp.emar` e\n",
    "  ON det.emar_id = e.emar_id\n",
    "WHERE e.hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "emardet = pd.read_gbq(query, \"lcp-internal\").sort_values('emar_seq')\n",
    "print(f'emardet: {emardet.columns}')\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT *\n",
    "FROM `physionet-data.mimiciv_icu.inputevents`\n",
    "WHERE hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "inp = pd.read_gbq(query, \"lcp-internal\").sort_values('starttime')\n",
    "add_offset_column(inp, admittime, 'starttime', 'offset')\n",
    "add_offset_column(inp, admittime, 'endtime', 'stop_offset')\n",
    "print(f'inp: {inp.columns}')\n",
    "\n",
    "# Load transfers\n",
    "query = f\"\"\"\n",
    "SELECT *\n",
    "FROM `physionet-data.mimiciv_hosp.transfers` tr\n",
    "WHERE hadm_id = {hadm_id}\n",
    "\"\"\"\n",
    "tr = pd.read_gbq(query, \"lcp-internal\").sort_values('intime')\n",
    "\n",
    "add_offset_column(tr, admittime, 'intime', 'in_offset')\n",
    "add_offset_column(tr, admittime, 'outtime', 'out_offset')\n",
    "\n",
    "\n",
    "#=== merge emar dataframes to simplify plot, focus on heparin/enoxaparin\n",
    "idx = emar['medication'].isin(['Heparin', 'Enoxaparin Sodium'])\n",
    "df_emar = emar.loc[idx].copy()\n",
    "\n",
    "# dose due is in the \"parent\" of the emar detail group\n",
    "# where parent_field_ordinal is null\n",
    "# only 1 of these per emar_id\n",
    "idx = emardet['parent_field_ordinal'].isnull()\n",
    "cols = [\n",
    "  'emar_id', 'administration_type', 'dose_due', 'dose_due_unit',\n",
    "  'complete_dose_not_given', 'will_remainder_of_dose_be_given'\n",
    "]\n",
    "df_emar = df_emar.merge(emardet.loc[idx, cols], on='emar_id', how='left')\n",
    "df_emar['dose_due'] = pd.to_numeric(df_emar['dose_due'])\n",
    "\n",
    "# add in the actual dose administered\n",
    "# since it can be across multiple formulary doses (e.g. multiple pills)\n",
    "# we aggregate the detail table\n",
    "# in heparin/enoxaparin case, we are probably aggregating over a single adm\n",
    "# note that for heparin, since it's a continuous infusion, dose_given is 0/None\n",
    "idx = emardet['parent_field_ordinal'].notnull()\n",
    "cols = ['emar_id', 'dose_given', 'dose_given_unit', 'product_amount_given', 'product_unit']\n",
    "em = emardet.loc[idx, cols].copy()\n",
    "\n",
    "# re-cast columns - sometimes drugs have string doses\n",
    "em['dose_given'] = pd.to_numeric(em['dose_given'], errors='coerce')\n",
    "em['product_amount_given'] = pd.to_numeric(em['product_amount_given'], errors='coerce')\n",
    "\n",
    "em = em.groupby('emar_id').agg(\n",
    "  dose_given=pd.NamedAgg(column='dose_given', aggfunc='sum'),\n",
    "  dose_given_unit=pd.NamedAgg(column='dose_given_unit', aggfunc='max'),\n",
    "  product_amount_given=pd.NamedAgg(column='product_amount_given', aggfunc='sum'),\n",
    "  product_unit=pd.NamedAgg(column='product_unit', aggfunc='max'),\n",
    ")\n",
    "\n",
    "df_emar = df_emar.merge(em, on='emar_id', how='left')\n",
    "df_emar.sort_values(['charttime', 'emar_seq'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a53cc7ba-c04d-445a-8ccd-781ce33f6a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[16, 12])\n",
    "\n",
    "# ICU documentation (inputevents) of heparin administration\n",
    "inp_labels = {\n",
    "  225152: 'Heparin (inputevents)'\n",
    "}\n",
    "\n",
    "for itemid, label in inp_labels.items():\n",
    "    idx = inp['itemid'] == itemid\n",
    "    dff = inp.loc[idx].sort_values('offset')\n",
    "    # first, plot bolus heparin\n",
    "    idx = dff['rate'].isnull()\n",
    "    plt.plot(dff.loc[idx, 'offset'], dff.loc[idx, 'amount'], 'P', markersize=16, color=colors[0], label='Heparin bolus (inputevents)')\n",
    "\n",
    "    for i, row in dff.iterrows():\n",
    "        rate = pd.to_numeric(row['rate'])\n",
    "        plt.plot([row['offset'], row['stop_offset']], [rate, rate], 's-', markersize=8, linewidth=4, color=colors[1], label=label)\n",
    "        # disable legend plotting in future rows\n",
    "        label = '__no_legend__'\n",
    "    # plt.fill_between([row['starttime'], row['endtime']], [0, 0], [rate, rate], color='#fc8d62', alpha=0.8)\n",
    "\n",
    "# eMAR documentation of heparin\n",
    "med = 'Heparin'\n",
    "idx_med = (df_emar['medication'] == med)\n",
    "# first, plot bolus separately\n",
    "idx = idx_med & (df_emar['administration_type'] == 'Heparin IV Bolus')\n",
    "plt.plot(df_emar.loc[idx, 'offset'], df_emar.loc[idx, 'dose_due'], 's', color=colors[2], markersize=16, alpha=0.5, label='Heparin bolus (eMAR)')\n",
    "\n",
    "idx = idx_med & (df_emar['administration_type'] != 'Heparin IV Bolus')\n",
    "plt.plot(df_emar.loc[idx, 'offset'], df_emar.loc[idx, 'dose_due'], 'o', color=colors[3], markersize=12, label='Heparin (eMAR)')\n",
    "\n",
    "# enoxaparin\n",
    "med = 'Enoxaparin Sodium'\n",
    "idx = (df_emar['medication'] == med)\n",
    "plt.plot(df_emar.loc[idx, 'offset'], df_emar.loc[idx, 'dose_due'], 'o', color=colors[4], markersize=12, label='Enoxaparin (eMAR)')\n",
    "\n",
    "\n",
    "# prescriptions documentation of heparin\n",
    "meds = ['Heparin', 'Heparin Sodium']\n",
    "med_colors = {\n",
    "    'Heparin': colors[5],\n",
    "    'Heparin Sodium': colors[6],\n",
    "    'Enoxaparin Sodium': colors[7]\n",
    "}\n",
    "idx = pr['drug'].isin(meds)\n",
    "df = pr.loc[idx].copy()\n",
    "\n",
    "for med in meds:\n",
    "    label = med + ' (prescriptions)'\n",
    "    color = med_colors[med]\n",
    "    idx = df['drug'] == med\n",
    "    n = 0\n",
    "    for i, row in df.loc[idx].iterrows():\n",
    "        if n > 0:\n",
    "          # do not add to the legend if we have already plotted at least once\n",
    "          med = '__no_legend__'\n",
    "\n",
    "        xi = [row['offset'], row['stop_offset']]\n",
    "        dose = row['dose_val_rx']\n",
    "        if '-' in dose:\n",
    "            # plot upper/lower ranges\n",
    "            dose_lower, dose_upper = dose.split('-')\n",
    "            dose_lower = int(dose_lower)\n",
    "            dose_upper = int(dose_upper)\n",
    "            plt.fill_between(xi, [dose_lower, dose_lower], [dose_upper, dose_upper], linewidth=4, color=color, alpha=0.5, label=label)\n",
    "        else:\n",
    "            dose = pd.to_numeric(dose.replace(',', ''))\n",
    "            dose = dose / 24\n",
    "            plt.plot(xi, [dose, dose], '^-', linewidth=4, markersize=16, color=color, label=label)\n",
    "        label = '__no_legend__'\n",
    "\n",
    "        n += 1\n",
    "\n",
    "# plot their ICU stay\n",
    "yloc = 4000\n",
    "for i, row in tr.iterrows():\n",
    "    plt.plot([row['in_offset'], row['out_offset']], [yloc, yloc], 'P-', linewidth=4, markersize=8, color=colors[7])\n",
    "    xloc = row['in_offset']\n",
    "    if row['careunit'] is None:\n",
    "        continue\n",
    "\n",
    "    if row['careunit'] == 'Emergency Department':\n",
    "        xloc -= 0.7\n",
    "    elif row['careunit'] == 'PACU':\n",
    "        xloc -= 0.5\n",
    "    elif 'CVICU' in row['careunit']:\n",
    "        xloc += 0.1\n",
    "    plt.text(xloc, yloc, row['careunit'], rotation=30, va='bottom', color=colors[7])\n",
    "    #if ('CCU' in row['careunit']) or ('ICU' in row['careunit']):\n",
    "    # plt.fill_between([row['in_offset'], row['out_offset']], [0, 0], [upper, upper], color='#ababab', alpha=0.2)\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Anticoagulation (units/hour)')\n",
    "\n",
    "plt.savefig('mimiciv_medication.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c50107ea-eca7-42a6-9d55-4dfb18aea88d",
   "metadata": {},
   "source": [
    "## Most frequent diagnoses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb8c4d16-ecb2-432c-bb18-8e847bec0e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>has_icu_admit</th>\n",
       "      <th>primary_dx</th>\n",
       "      <th>icd_code</th>\n",
       "      <th>icd_version</th>\n",
       "      <th>long_title</th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>29420</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Dementia, unspecified, without behavioral dist...</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>V6284</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Suicidal ideation</td>\n",
       "      <td>7970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2979</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Unspecified paranoid state</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30500</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Alcohol abuse, unspecified</td>\n",
       "      <td>6223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2809</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Iron deficiency anemia, unspecified</td>\n",
       "      <td>6054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   has_icu_admit  primary_dx icd_code  icd_version  \\\n",
       "0              0           1    29420          9.0   \n",
       "1              0           0    V6284          9.0   \n",
       "2              0           0     2979          9.0   \n",
       "3              0           0    30500          9.0   \n",
       "4              0           0     2809          9.0   \n",
       "\n",
       "                                          long_title     n  \n",
       "0  Dementia, unspecified, without behavioral dist...    63  \n",
       "1                                  Suicidal ideation  7970  \n",
       "2                         Unspecified paranoid state    68  \n",
       "3                         Alcohol abuse, unspecified  6223  \n",
       "4                Iron deficiency anemia, unspecified  6054  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 2 Most frequent diagnoses as coded using the ICD codes.\n",
    "query = \"\"\"\n",
    "WITH grp AS\n",
    "(\n",
    "SELECT a.hadm_id\n",
    ", CASE WHEN icu.hadm_id IS NOT NULL THEN 1 ELSE 0 END AS has_icu_admit\n",
    ", CASE WHEN dia.seq_num > 1 THEN 0 ELSE 1 END AS primary_dx\n",
    ", dia.icd_code\n",
    ", dia.icd_version\n",
    ", d_dx.long_title\n",
    "FROM `lcp-internal.mimic_hosp.admissions` a\n",
    "LEFT JOIN `lcp-internal.mimic_hosp.diagnoses_icd` dia ON a.hadm_id = dia.hadm_id\n",
    "LEFT JOIN `lcp-internal.mimic_hosp.d_icd_diagnoses` d_dx ON dia.icd_code = d_dx.icd_code AND dia.icd_version = d_dx.icd_version\n",
    "-- join to icustays and flag those which have an ICU stay\n",
    "LEFT JOIN ( SELECT DISTINCT hadm_id FROM `lcp-internal.mimic_icu.icustays` ) icu ON a.hadm_id = icu.hadm_id\n",
    ")\n",
    "SELECT has_icu_admit, primary_dx, icd_code, icd_version, long_title\n",
    ", COUNT(*) AS n\n",
    "FROM grp\n",
    "GROUP BY 1, 2, 3, 4, 5\n",
    "\"\"\"\n",
    "\n",
    "dx = pd.read_gbq(query, \"lcp-internal\")\n",
    "dx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eb106c8c-f5e1-42ee-9770-849ca187aa76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 dx for stays with an ICU admission:\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icd_code</th>\n",
       "      <th>long_title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <th>Unspecified essential hypertension</th>\n",
       "      <td>17489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2724</th>\n",
       "      <th>Other and unspecified hyperlipidemia</th>\n",
       "      <td>13443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E785</th>\n",
       "      <th>Hyperlipidemia, unspecified</th>\n",
       "      <td>11659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42731</th>\n",
       "      <th>Atrial fibrillation</th>\n",
       "      <td>10950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I10</th>\n",
       "      <th>Essential (primary) hypertension</th>\n",
       "      <td>10627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4280</th>\n",
       "      <th>Congestive heart failure, unspecified</th>\n",
       "      <td>10250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41401</th>\n",
       "      <th>Coronary atherosclerosis of native coronary artery</th>\n",
       "      <td>9666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z87891</th>\n",
       "      <th>Personal history of nicotine dependence</th>\n",
       "      <td>9067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5849</th>\n",
       "      <th>Acute kidney failure, unspecified</th>\n",
       "      <td>8812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53081</th>\n",
       "      <th>Esophageal reflux</th>\n",
       "      <td>7714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 n\n",
       "icd_code long_title                                               \n",
       "4019     Unspecified essential hypertension                  17489\n",
       "2724     Other and unspecified hyperlipidemia                13443\n",
       "E785     Hyperlipidemia, unspecified                         11659\n",
       "42731    Atrial fibrillation                                 10950\n",
       "I10      Essential (primary) hypertension                    10627\n",
       "4280     Congestive heart failure, unspecified               10250\n",
       "41401    Coronary atherosclerosis of native coronary artery   9666\n",
       "Z87891   Personal history of nicotine dependence              9067\n",
       "5849     Acute kidney failure, unspecified                    8812\n",
       "53081    Esophageal reflux                                    7714"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 dx for stays without an ICU admission:\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
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       "      <td>85032</td>\n",
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       "    <tr>\n",
       "      <th>2724</th>\n",
       "      <th>Other and unspecified hyperlipidemia</th>\n",
       "      <td>53899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I10</th>\n",
       "      <th>Essential (primary) hypertension</th>\n",
       "      <td>43766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53081</th>\n",
       "      <th>Esophageal reflux</th>\n",
       "      <td>40959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E785</th>\n",
       "      <th>Hyperlipidemia, unspecified</th>\n",
       "      <td>39379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000</th>\n",
       "      <th>Diabetes mellitus without mention of complication, type II or unspecified type, not stated as uncontrolled</th>\n",
       "      <td>35644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Z87891</th>\n",
       "      <th>Personal history of nicotine dependence</th>\n",
       "      <td>31704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <th>Depressive disorder, not elsewhere classified</th>\n",
       "      <td>31210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K219</th>\n",
       "      <th>Gastro-esophageal reflux disease without esophagitis</th>\n",
       "      <td>28961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41401</th>\n",
       "      <th>Coronary atherosclerosis of native coronary artery</th>\n",
       "      <td>26462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 n\n",
       "icd_code long_title                                               \n",
       "4019     Unspecified essential hypertension                  85032\n",
       "2724     Other and unspecified hyperlipidemia                53899\n",
       "I10      Essential (primary) hypertension                    43766\n",
       "53081    Esophageal reflux                                   40959\n",
       "E785     Hyperlipidemia, unspecified                         39379\n",
       "25000    Diabetes mellitus without mention of complicati...  35644\n",
       "Z87891   Personal history of nicotine dependence             31704\n",
       "311      Depressive disorder, not elsewhere classified       31210\n",
       "K219     Gastro-esophageal reflux disease without esopha...  28961\n",
       "41401    Coronary atherosclerosis of native coronary artery  26462"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 *primary* dx for stays with an ICU admission:\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>icd_code</th>\n",
       "      <th>long_title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0389</th>\n",
       "      <th>Unspecified septicemia</th>\n",
       "      <td>2290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41401</th>\n",
       "      <th>Coronary atherosclerosis of native coronary artery</th>\n",
       "      <td>2112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A419</th>\n",
       "      <th>Sepsis, unspecified organism</th>\n",
       "      <td>1884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4241</th>\n",
       "      <th>Aortic valve disorders</th>\n",
       "      <td>1162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41071</th>\n",
       "      <th>Subendocardial infarction, initial episode of care</th>\n",
       "      <td>1041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I214</th>\n",
       "      <th>Non-ST elevation (NSTEMI) myocardial infarction</th>\n",
       "      <td>892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <th>Intracerebral hemorrhage</th>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I2510</th>\n",
       "      <th>Atherosclerotic heart disease of native coronary artery without angina pectoris</th>\n",
       "      <td>790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51881</th>\n",
       "      <th>Acute respiratory failure</th>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I671</th>\n",
       "      <th>Cerebral aneurysm, nonruptured</th>\n",
       "      <td>607</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                n\n",
       "icd_code long_title                                              \n",
       "0389     Unspecified septicemia                              2290\n",
       "41401    Coronary atherosclerosis of native coronary artery  2112\n",
       "A419     Sepsis, unspecified organism                        1884\n",
       "4241     Aortic valve disorders                              1162\n",
       "41071    Subendocardial infarction, initial episode of care  1041\n",
       "I214     Non-ST elevation (NSTEMI) myocardial infarction      892\n",
       "431      Intracerebral hemorrhage                             822\n",
       "I2510    Atherosclerotic heart disease of native coronar...   790\n",
       "51881    Acute respiratory failure                            773\n",
       "I671     Cerebral aneurysm, nonruptured                       607"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 *primary* dx for stays without an ICU admission:\n"
     ]
    },
    {
     "data": {
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       "      <th>78650</th>\n",
       "      <th>Chest pain, unspecified</th>\n",
       "      <td>7320</td>\n",
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       "    <tr>\n",
       "      <th>78659</th>\n",
       "      <th>Other chest pain</th>\n",
       "      <td>5199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30500</th>\n",
       "      <th>Alcohol abuse, unspecified</th>\n",
       "      <td>4577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41401</th>\n",
       "      <th>Coronary atherosclerosis of native coronary artery</th>\n",
       "      <td>3630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <th>Depressive disorder, not elsewhere classified</th>\n",
       "      <td>3567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <th>Pneumonia, organism unspecified</th>\n",
       "      <td>3165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5849</th>\n",
       "      <th>Acute kidney failure, unspecified</th>\n",
       "      <td>3149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F329</th>\n",
       "      <th>Major depressive disorder, single episode, unspecified</th>\n",
       "      <td>3045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V5811</th>\n",
       "      <th>Encounter for antineoplastic chemotherapy</th>\n",
       "      <td>2914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5990</th>\n",
       "      <th>Urinary tract infection, site not specified</th>\n",
       "      <td>2836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                n\n",
       "icd_code long_title                                              \n",
       "78650    Chest pain, unspecified                             7320\n",
       "78659    Other chest pain                                    5199\n",
       "30500    Alcohol abuse, unspecified                          4577\n",
       "41401    Coronary atherosclerosis of native coronary artery  3630\n",
       "311      Depressive disorder, not elsewhere classified       3567\n",
       "486      Pneumonia, organism unspecified                     3165\n",
       "5849     Acute kidney failure, unspecified                   3149\n",
       "F329     Major depressive disorder, single episode, unsp...  3045\n",
       "V5811    Encounter for antineoplastic chemotherapy           2914\n",
       "5990     Urinary tract infection, site not specified         2836"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = dx['has_icu_admit'] == 1\n",
    "print('Top 10 dx for stays with an ICU admission:')\n",
    "grp = dx.loc[idx].groupby(['icd_code', 'long_title'])[['n']].sum().sort_values('n', ascending=False)\n",
    "display(grp.head(n=10))\n",
    "\n",
    "print('Top 10 dx for stays without an ICU admission:')\n",
    "grp = dx.loc[~idx].groupby(['icd_code', 'long_title'])[['n']].sum().sort_values('n', ascending=False)\n",
    "display(grp.head(n=10))\n",
    "\n",
    "idx = (dx['has_icu_admit'] == 1) & (dx['primary_dx'] == 1)\n",
    "print('Top 10 *primary* dx for stays with an ICU admission:')\n",
    "grp = dx.loc[idx].groupby(['icd_code', 'long_title'])[['n']].sum().sort_values('n', ascending=False)\n",
    "display(grp.head(n=10))\n",
    "\n",
    "idx = (dx['has_icu_admit'] == 0) & (dx['primary_dx'] == 1)\n",
    "print('Top 10 *primary* dx for stays without an ICU admission:')\n",
    "grp = dx.loc[idx].groupby(['icd_code', 'long_title'])[['n']].sum().sort_values('n', ascending=False)\n",
    "display(grp.head(n=10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3123f7-ae1e-4425-90b9-528b3a41cf1b",
   "metadata": {},
   "source": [
    "### Data completion for ICU stays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5985b975-2d33-44c6-a056-9353441a83f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>has_icu_stay</th>\n",
       "      <th>n_transfers</th>\n",
       "      <th>n_diagnoses_icd</th>\n",
       "      <th>n_drgcodes</th>\n",
       "      <th>n_emar</th>\n",
       "      <th>n_hcpcsevents</th>\n",
       "      <th>n_labevents</th>\n",
       "      <th>n_microbiologyevents</th>\n",
       "      <th>n_omr</th>\n",
       "      <th>n_pharmacy</th>\n",
       "      <th>n_poe</th>\n",
       "      <th>n_prescriptions</th>\n",
       "      <th>n_procedures_icd</th>\n",
       "      <th>n_services</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadm_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22942076</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>313</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>123</td>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20668418</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>411</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>218</td>\n",
       "      <td>113</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21095812</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2260</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>251</td>\n",
       "      <td>743</td>\n",
       "      <td>302</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22580355</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1628</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>781</td>\n",
       "      <td>336</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25020332</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>113</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>65</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          has_icu_stay  n_transfers  n_diagnoses_icd  n_drgcodes  n_emar  \\\n",
       "hadm_id                                                                    \n",
       "22942076             1            4               10           2       0   \n",
       "20668418             0            6               14           2       0   \n",
       "21095812             1            2               25           2       0   \n",
       "22580355             1            2               20           2       0   \n",
       "25020332             0            2               11           2     113   \n",
       "\n",
       "          n_hcpcsevents  n_labevents  n_microbiologyevents  n_omr  n_pharmacy  \\\n",
       "hadm_id                                                                         \n",
       "22942076              0          313                     7      0          40   \n",
       "20668418              0          411                     1      0         104   \n",
       "21095812              0         2260                   118      0         251   \n",
       "22580355              0         1628                   184      0         284   \n",
       "25020332              0           63                     6      0          25   \n",
       "\n",
       "          n_poe  n_prescriptions  n_procedures_icd  n_services  \n",
       "hadm_id                                                         \n",
       "22942076    123               63                 3           1  \n",
       "20668418    218              113                 8           2  \n",
       "21095812    743              302                 7           1  \n",
       "22580355    781              336                 8           1  \n",
       "25020332     65               26                 7           1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT a.hadm_id\n",
    ", CASE WHEN icu.hadm_id IS NOT NULL THEN 1 ELSE 0 END AS has_icu_stay\n",
    ",tra.n AS n_transfers\n",
    ",dia.n AS n_diagnoses_icd\n",
    ",drg.n AS n_drgcodes\n",
    ",ema.n AS n_emar\n",
    ",hcp.n AS n_hcpcsevents\n",
    ",lab.n AS n_labevents\n",
    ",mic.n AS n_microbiologyevents\n",
    ",omr.n AS n_omr\n",
    ",pha.n AS n_pharmacy\n",
    ",poe.n AS n_poe\n",
    ",pre.n AS n_prescriptions\n",
    ",pro.n AS n_procedures_icd\n",
    ",ser.n AS n_services\n",
    "FROM `lcp-internal.mimic_hosp.admissions` a\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.transfers` GROUP BY hadm_id ) tra ON a.hadm_id = tra.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.diagnoses_icd` GROUP BY hadm_id ) dia ON a.hadm_id = dia.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.drgcodes` GROUP BY hadm_id ) drg ON a.hadm_id = drg.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.emar` GROUP BY hadm_id ) ema ON a.hadm_id = ema.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.hcpcsevents` GROUP BY hadm_id ) hcp ON a.hadm_id = hcp.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.labevents` GROUP BY hadm_id ) lab ON a.hadm_id = lab.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.microbiologyevents` GROUP BY hadm_id ) mic ON a.hadm_id = mic.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.pharmacy` GROUP BY hadm_id ) pha ON a.hadm_id = pha.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.poe` GROUP BY hadm_id ) poe ON a.hadm_id = poe.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.prescriptions` GROUP BY hadm_id ) pre ON a.hadm_id = pre.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.procedures_icd` GROUP BY hadm_id ) pro ON a.hadm_id = pro.hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.services` GROUP BY hadm_id ) ser ON a.hadm_id = ser.hadm_id\n",
    "-- below table needs a join to hadm_id\n",
    "LEFT JOIN ( SELECT hadm_id, COUNT(*) AS N FROM `lcp-internal.mimic_hosp.omr` t INNER JOIN `lcp-internal.mimic_hosp.admissions` adm ON t.subject_id = adm.subject_id AND t.chartdate BETWEEN adm.admittime AND adm.dischtime GROUP BY hadm_id ) omr ON a.hadm_id = omr.hadm_id\n",
    "-- join to icustays and flag those which have an ICU stay\n",
    "LEFT JOIN ( SELECT DISTINCT hadm_id FROM `lcp-internal.mimic_icu.icustays` ) icu ON a.hadm_id = icu.hadm_id\n",
    "\"\"\"\n",
    "\n",
    "tbl_count = pd.read_gbq(query, \"lcp-internal\")\n",
    "tbl_count.set_index('hadm_id', inplace=True)\n",
    "# fill empty values with 0 -> no observations found\n",
    "for col in tbl_count.columns:\n",
    "    tbl_count[col] = tbl_count[col].fillna(0)\n",
    "    tbl_count[col] = tbl_count[col].astype(int)\n",
    "\n",
    "tbl_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4c16c4b0-a052-4d5d-b172-2465517ee057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data completion</th>\n",
       "      <th>5th percentile</th>\n",
       "      <th>Median number of observations</th>\n",
       "      <th>95th percentile</th>\n",
       "      <th>No data (ICU subset)</th>\n",
       "      <th>5th percentile (ICU subset)</th>\n",
       "      <th>Median number of observations (ICU subset)</th>\n",
       "      <th>95th percentile (ICU subset)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>transfers</th>\n",
       "      <td>454,324 (100.00%)</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>69,639 (100.00%)</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnoses_icd</th>\n",
       "      <td>453,925 (99.91%)</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>25</td>\n",
       "      <td>69,618 (99.97%)</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drgcodes</th>\n",
       "      <td>328,381 (72.28%)</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>69,029 (99.12%)</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emar</th>\n",
       "      <td>175,065 (38.53%)</td>\n",
       "      <td>9</td>\n",
       "      <td>73</td>\n",
       "      <td>505</td>\n",
       "      <td>34,675 (49.79%)</td>\n",
       "      <td>18</td>\n",
       "      <td>175</td>\n",
       "      <td>1034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hcpcsevents</th>\n",
       "      <td>125,350 (27.59%)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>589 (0.85%)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labevents</th>\n",
       "      <td>369,601 (81.35%)</td>\n",
       "      <td>11</td>\n",
       "      <td>84</td>\n",
       "      <td>583</td>\n",
       "      <td>69,036 (99.13%)</td>\n",
       "      <td>49</td>\n",
       "      <td>259</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>microbiologyevents</th>\n",
       "      <td>172,662 (38.00%)</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>58,171 (83.53%)</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omr</th>\n",
       "      <td>60,845 (13.39%)</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4,056 (5.82%)</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pharmacy</th>\n",
       "      <td>384,772 (84.69%)</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>105</td>\n",
       "      <td>69,497 (99.80%)</td>\n",
       "      <td>18</td>\n",
       "      <td>62</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poe</th>\n",
       "      <td>448,026 (98.61%)</td>\n",
       "      <td>12</td>\n",
       "      <td>60</td>\n",
       "      <td>276</td>\n",
       "      <td>69,167 (99.32%)</td>\n",
       "      <td>57</td>\n",
       "      <td>179</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prescriptions</th>\n",
       "      <td>384,772 (84.69%)</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>126</td>\n",
       "      <td>69,497 (99.80%)</td>\n",
       "      <td>20</td>\n",
       "      <td>75</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>procedures_icd</th>\n",
       "      <td>241,358 (53.12%)</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>57,510 (82.58%)</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>454,324 (100.00%)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>69,639 (100.00%)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Data completion  5th percentile  \\\n",
       "transfers           454,324 (100.00%)               2   \n",
       "diagnoses_icd        453,925 (99.91%)               2   \n",
       "drgcodes             328,381 (72.28%)               1   \n",
       "emar                 175,065 (38.53%)               9   \n",
       "hcpcsevents          125,350 (27.59%)               1   \n",
       "labevents            369,601 (81.35%)              11   \n",
       "microbiologyevents   172,662 (38.00%)               1   \n",
       "omr                   60,845 (13.39%)               2   \n",
       "pharmacy             384,772 (84.69%)               9   \n",
       "poe                  448,026 (98.61%)              12   \n",
       "prescriptions        384,772 (84.69%)               9   \n",
       "procedures_icd       241,358 (53.12%)               1   \n",
       "services            454,324 (100.00%)               1   \n",
       "\n",
       "                    Median number of observations  95th percentile  \\\n",
       "transfers                                       3                6   \n",
       "diagnoses_icd                                  10               25   \n",
       "drgcodes                                        2                2   \n",
       "emar                                           73              505   \n",
       "hcpcsevents                                     1                3   \n",
       "labevents                                      84              583   \n",
       "microbiologyevents                              3               32   \n",
       "omr                                             3                6   \n",
       "pharmacy                                       25              105   \n",
       "poe                                            60              276   \n",
       "prescriptions                                  27              126   \n",
       "procedures_icd                                  2                8   \n",
       "services                                        1                2   \n",
       "\n",
       "                   No data (ICU subset)  5th percentile (ICU subset)  \\\n",
       "transfers              69,639 (100.00%)                            3   \n",
       "diagnoses_icd           69,618 (99.97%)                            5   \n",
       "drgcodes                69,029 (99.12%)                            2   \n",
       "emar                    34,675 (49.79%)                           18   \n",
       "hcpcsevents                 589 (0.85%)                            1   \n",
       "labevents               69,036 (99.13%)                           49   \n",
       "microbiologyevents      58,171 (83.53%)                            1   \n",
       "omr                       4,056 (5.82%)                            1   \n",
       "pharmacy                69,497 (99.80%)                           18   \n",
       "poe                     69,167 (99.32%)                           57   \n",
       "prescriptions           69,497 (99.80%)                           20   \n",
       "procedures_icd          57,510 (82.58%)                            1   \n",
       "services               69,639 (100.00%)                            1   \n",
       "\n",
       "                    Median number of observations (ICU subset)  \\\n",
       "transfers                                                    5   \n",
       "diagnoses_icd                                               16   \n",
       "drgcodes                                                     2   \n",
       "emar                                                       175   \n",
       "hcpcsevents                                                  1   \n",
       "labevents                                                  259   \n",
       "microbiologyevents                                           4   \n",
       "omr                                                          3   \n",
       "pharmacy                                                    62   \n",
       "poe                                                        179   \n",
       "prescriptions                                               75   \n",
       "procedures_icd                                               3   \n",
       "services                                                     1   \n",
       "\n",
       "                    95th percentile (ICU subset)  \n",
       "transfers                                      9  \n",
       "diagnoses_icd                                 33  \n",
       "drgcodes                                       2  \n",
       "emar                                        1034  \n",
       "hcpcsevents                                    3  \n",
       "labevents                                   1349  \n",
       "microbiologyevents                            47  \n",
       "omr                                            8  \n",
       "pharmacy                                     196  \n",
       "poe                                          628  \n",
       "prescriptions                                244  \n",
       "procedures_icd                                12  \n",
       "services                                       3  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_list = tbl_count.columns[1:]\n",
    "tbl_agg = pd.DataFrame(index=table_list)\n",
    "\n",
    "tbl_agg['Data completion'] = (tbl_count > 0).sum()\n",
    "N = tbl_count.shape[0]\n",
    "tbl_agg['Data completion'] = tbl_agg['Data completion'].apply(lambda x: f'{x:,} ({x/N:3.2%})')\n",
    "\n",
    "# iterate over 3 quantile values and output to the table\n",
    "quantiles_cols = [[0.05, '5th percentile'], [0.5, 'Median number of observations'], [0.95, '95th percentile']]\n",
    "for q, quantile_col_name in quantiles_cols:\n",
    "    tbl_agg[quantile_col_name] = 0\n",
    "    for col in table_list:\n",
    "        idx = tbl_count[col] > 0\n",
    "        tbl_agg.loc[col, quantile_col_name] = tbl_count.loc[idx, col].quantile(q)\n",
    "\n",
    "\n",
    "# repeat for ICU stays\n",
    "\n",
    "idx_icu = tbl_count['has_icu_stay'] == 1\n",
    "\n",
    "tbl_agg['No data (ICU subset)'] = 0\n",
    "for col in table_list:\n",
    "    tbl_agg.loc[col, 'No data (ICU subset)'] = (tbl_count.loc[idx_icu, col] > 0).sum()\n",
    "\n",
    "N = idx_icu.sum()\n",
    "tbl_agg['No data (ICU subset)'] = tbl_agg['No data (ICU subset)'].apply(lambda x: f'{x:,} ({x/N:3.2%})')\n",
    "\n",
    "for q, quantile_col_name in quantiles_cols:\n",
    "    quantile_col_name += ' (ICU subset)'\n",
    "    tbl_agg[quantile_col_name] = 0\n",
    "    for col in table_list:\n",
    "        idx = (tbl_count[col] > 0) & (idx_icu)\n",
    "        tbl_agg.loc[col, quantile_col_name] = tbl_count.loc[idx, col].quantile(q)\n",
    "\n",
    "tbl_agg.index = [c[2:] for c in tbl_agg.index]\n",
    "tbl_agg"
   ]
  }
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